Welcome

The summer school in Energy Informatics 2024 is designed to empower doctoral and master students, along with professionals in the energy sector, to develop advanced skills in understanding and managing the impacts of natural disasters and grid disturbances on energy systems. The program is particularly focused on leveraging data processing techniques and clustering algorithms to analyze patterns and assess impacts.

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The summer school is organized in cooperation with Energy Informatics.Academy Conference 2024 (EI.A 2024) (21-22 October 2024). 

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Key Topics of the 2024 Summer School:

• Impact Analysis of Natural Disasters on Energy Systems: Understanding the dynamics and consequences of natural disasters on energy grids.

• Data Processing for Disaster Impact Analysis: Techniques and tools for processing large-scale data related to energy systems and disaster impacts.

• Clustering Algorithms for Pattern Recognition: Developing and applying clustering algorithms to discern patterns in energy system disruptions and recovery.

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Program Highlights:

• Expert-led Lectures: Gain insights from domain experts on the complexities of energy systems and their vulnerability to natural events.

• Hands-on Exercises: Engage in practical sessions using real-world data sets to understand the nuances of data processing and algorithm development.

• Case Studies and Group Discussions: Analyze case studies of past natural disasters and participate in discussions on innovative solutions and strategies.

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Prerequisites:

• Basic understanding of Python programming language.

• Basic knowledge of energy systems.

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Duration and Structure:

• A 2-day intensive module on Data Processing and Clustering Techniques for understanding the patterns in energy system disruptions caused by natural disasters.

• Interactive sessions focusing on Practical Applications and Case Study Analyses.

ECTs:

The program with a scope of 2 ECTS credits is designed to enhance PhD researchers' academic and professional development.

By the end of this summer school, participants will acquire a profound understanding of how data processing and clustering algorithms can be effectively utilized to analyze and mitigate the impacts of natural disasters on energy systems, thereby contributing to the development of more resilient energy infrastructures.

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